Content-based fauna image retrieval system

Many animal species exist in this world and there are always new species being discovered each year. Therefore, it is very important that these valuable species be documented properly to be referred to in future. Numerous information retrieval systems for managing and documenting animal species today only allow users to search animal images and descriptions online via text-based input. Therefore, people without knowledge on the animal species or without Internet access are not able to search using the systems. Motivated by these issues, the focus of this work is to construct a colour-shape content-based image representation for fauna. Two orders of the Colour Moment are used to represent the colour feature while the i-means approach is used to represent the shape feature. Based on the conducted quantitative and qualitative studies, the proposed fusion method together with the Content-based Image Retrieval (CBIR) system are found to be very effective in retrieving animal images similar to the given query, able to provide reliable and useful information on animal species, an easy system to interact with, and has easy to understand and user-friendly interfaces

ANALISA PENERAPAN METODE MULTI-SCALE EDGE DETECTION DAN COLOR HISTOGRAM DALAM PROSES PENCARIAN GAMBAR

ABSTRAKSI: Content-based image retrival adalah pencarian gambar dengan memanfaatkan fitur ciri yang ada pada gambar. Fitur ciri tersebut dapat berupa bentuk, warna, tekstur, dan lain-lain. Ekstrasi ciri yang dibutuhkan untuk pencarian berbeda-beda tergantung dari domain gambar yang akan dicari. Dengan menggunakan ekstraksi ciri yang tepat, hasil pencarian dapat menjadi lebih baik. Untuk gambar dengan domain flora dan fauna, ekstraksi ciri yang dapat digunakan adalah ekstraksi sisi dan ekstraksi warna.Dari permasalahan yang dikemukakan di atas maka dibangunlah sebuah sistem image retrieval dengan memanfaatkan fitur warna dan fitur sisi dari citra. fitur warna citra diekstrak dengan menggunakan color histogram. Sedangkan fitur sisi dari citra akan diekstrak dengan menggunakan multi-scale edge detection. Dari sistem yang dibangun ini kemudian dilihat bagaimana performansi pencarian gambar yang dihasilkan serta faktor-faktor apa saja yang mempengaruhi hasil pencarian.Algoritma multi-scale edge detection adalah metode mencari representasi edge sebuah gambar dengan menggunakan sebuah operator edge detection akan tetapi proses pencarian edge dilakukan beberapa kali dengan perbedaan nilai Gaussian blur sehingga diperoleh hasil ekstraksi edge yang lebih baik. Color histogram yang digunakan dibagi berdasarkan color space dan representasi histogramnya. Dengan memanfaatkan edge detection dan color histogram diharapkan hasil pencarian citra bisa menjadi lebih baik.Dari hasil pengujian yang dilakukan, dengan menggabungkan ekstraksi sisi dan ekstraksi warna, performansi pencarian citra dapat ditingkatkan. Peningkatan ini dapat dilihat dari peningkatan nilai precision yang diperoleh. Besarnya nilai precision dipengaruhi oleh beberapa faktor, seperti komposisi warna citra dan warna background, detail edge yang dihasilkan, metode pengukuran jarak antara query dan database, dan bobot untuk similarity yang diberikan untuk ekstraksi ciri.Kata Kunci : image retrieval, ekstraksi ciri, multi-scale edge detection, color histogramABSTRACT: Content-based image retrieval is an image search using feature extraction of image characteristic. The Image characteristic is defined by shape, color, texture, and else. Feature extraction that required is different depends on the image domain. Using the right feature extraction of image could obtain a better result. For images which contain flora and fauna, the feature extraction that could be use is edge extraction and color extraction.From the problem that explained above, thus, is built an image retrieval system using color extraction and edge extraction from image. Color feature of image is extracted by using color histogram. Whereas, the edge feature of image is extracted by using multi-scale detection. Afterwards, from the system which built, the performance of image retrieval along with the factors that affect the image retrieval can be yielded.Multi-scale detection algorithm is a searching method to represent an edge of image using edge detection operator, however, edge detection process retrieved several time with the different value of Gaussian blur, so the better edge extraction is obtained. Color Histogram that used based on color space and histogram representation. By using edge detection and color histogram, the image retrieval is expected better.From a trial which had done, combining edge extraction and color extraction could enhance performance of image retrieval. This enhancement is perceived by the improvement of precision value. The precision value is affected by of several factors such composition of image color and background, edge detail, distance measurement between query and database, and weight of similarity that given for feature extraction.Keyword: image retrieval, feature extraction, multi-scale edge detection, color histogra

Temu Kembali Citra Tenun Nusa Tenggara Timur menggunakan Esktraksi Fitur yang Robust terhadap Perubahan Skala, Rotasi, dan Pencahayaan

Ragam motif pada tenun Nusa Tenggara Timur (NTT) seperti flora, fauna dan geometris menjadi suatu keunikan yang dapat membedakan daerah asal dan jenis dari tenun tersebut. Pada penelitian ini, sistem temu kembali citra berbasis isi atau Content-Based Image Retrieval (CBIR) diimplementasikan pada citra tenun NTT sehingga user dapat mencari citra tenun pada database menggunakan citra query berdasarkan fitur visual yang terkandung dalam citra. Seringkali citra query yang diinputkan user memiliki skala, rotasi dan pencahayaan yang bervariasi, sehingga diperlukan suatu metode ektraksi fitur yang dapat mengakomodasi variasi tersebut. Sistem temu kembali citra tenun pada penelitian ini menggunakan model Bag of Visual Words (BoVW) dari keypoints pada citra yang diekstrak dengan metode Speeded Up Robust Feature (SURF). BoVW dibangun menggunakan K-Means untuk menghasilkan visual vocabulary dari keypoints pada seluruh citra training. Representasi BoVW diharapkan dapat menangani variasi skala dan rotasi pada citra. Sedangkan untuk mengatasi variasi pencahayaan pada citra, dilakukan perbaikan kualitas citra dengan menggunakan Contrast Limited Adaptive Histogram Equalization (CLAHE). Percobaan dilakukan dengan membandingkan kinerja dari representasi BoVW yang dibangun menggunakan fitur SURF dengan Maximally Stable Extremal Regions (MSER) pada temu kembali citra tenun. Hasil uji coba menunjukkan bahwa metode SURF menghasilkan rata-rata akurasi 89,86% dan waktu komputasi 9,94 detik, sedangkan MSER menghasilkan rata-rata akurasi 84,04% dan waktu komputasi 1,95 detik. AbstractThe variety of motifs in East Nusa Tenggara tenun such as flora, fauna and geometric is an unique thing that can distinguish the region of origin and type of the tenun. In this study, the Content-Based Image Retrieval (CBIR) system is implemented in the tenun image. With Content-based techniques Users can search tenun images on the image database by using query images based on visual features contained in the image. Often the query image that the user enters has a different scale, rotation and lighting, so a feature extraction method is needed that can accommodate these differences. The tenun image retrieval system in this study used the Bag of Visual Words (BoVW) model of the keypoints in the extracted image using the Speeded Up Robust Feature (SURF) method. BoVW was built using K-Means to produce visual vocabulary from keypoints on all training images. The representation of BoVW is expected to be able to handle scale variations and rotations in images. Whereas to overcome the lighting variations in the image, image quality improvement is done by using Contrast Limited Adaptive Histogram Equalization (CLAHE). The experiment was conducted by comparing the performance of the BoVW representation which was built using the SURF feature with Maximally Stable Extremal Regions (MSER) at the tenun image retrieval. The results of the trial showed that SURF obtained higher accuracy in all conditions of tenun image data with an average value of 89.86% whereas MSER obtained an average accuracy value of 84.04%. But MSER's computation time is 1.95 seconds faster than SURF which is 9.94 seconds

Soil biodiversity: functions, threats and tools for policy makers

Human societies rely on the vast diversity of benefits provided by nature, such as food, fibres, construction materials, clean water, clean air and climate regulation. All the elements required for these ecosystem services depend on soil, and soil biodiversity is the driving force behind their regulation. With 2010 being the international year of biodiversity and with the growing attention in Europe on the importance of soils to remain healthy and capable of supporting human activities sustainably, now is the perfect time to raise awareness on preserving soil biodiversity. The objective of this report is to review the state of knowledge of soil biodiversity, its functions, its contribution to ecosystem services and its relevance for the sustainability of human society. In line with the definition of biodiversity given in the 1992 Rio de Janeiro Convention, soil biodiversity can be defined as the variation in soil life, from genes to communities, and the variation in soil habitats, from micro-aggregates to entire landscapes. Bio Intelligence Service, IRD, and NIOO, Report for European Commission (DG Environment

Digital archiving of manuscripts and other heritage items for conservation and information retrieval

Expression of cultural heritage looking from the informatics angle falls into text, images, video and sound categories. ICT can be used to conserve all these heritage items like; the text information consisting of palm leaf manuscripts, stone tablets, handwritten paper documents, old printed records, books, microfilms, fiche etc, images including paintings, drawings, photographs and the like, sound items which includes musical concerts, poetry recitations, chanting of mantras, talks of important persons etc, and video items like archival films historical importance. To retrieve required information from such a large mass of materials in different formats and to transmit them across space and time, there are several limitations. Digital technology allows hitherto unavailable facilities for durable storage and speedy and efficient transmission / retrieval of information contained in all the above formats. Hypertext and hypermedia features of digital media enable integrating text with graphics, sound, video and animation. This paper discusses the international and national efforts for digitizing heritage items, digital archiving solutions available, the possibilities of the media, and the need to follow standards prescribed by organizations like UNESCO to enable easy exchange and pooling of information and documents generated in digital archiving systems at national and international level. The need to develop language technology for local scripts for organizing and preserving our cultural heritage is also stressed

Guidelines for the study of the epibenthos of subtidal environments

These Guidelines for the Study of the Epibenthos of Subtidal Environments document a range of sampling gears and procedures for epibenthos studies that meet a variety of needs. The importance of adopting consistent sampling and analytical practices is highlighted. Emphasis is placed on ship‐based techniques for surveys of coastal and offshore shelf environments, but diver‐assisted surveys are also considered

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata

Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD) 201

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability